One of the most important aspects that will be studied in this work is the architecture of deep neural networks. The majority of DL approaches that are present in the literature are based on fully connected networks, which are inefficient and prone to overfitting when applied to high-dimensional inputs. If such large inputs are structured, they fall under the umbrella of the geometric deep learning (GDL) paradigm [1]}–a concept that has been gaining increasing interest in recent years. In this work, we will compare three architectures that can efficiently deal with high-dimensional structured inputs: convolutional neural networks (CNNs), graph neural networks (GNNs), and attention-based networks.
